Heap Dynamics Statistical Analysis for Memory Leak Root Cause Detection
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Solution Overview
Problem
Existing memory leak analysis tools are limited by their inability to monitor the heap in real-time, leading to false positives and the complexity of identifying multiple contributing software problems, making it difficult to detect memory leaks in large-scale applications.
Innovation Solution
A method that dynamically collects multiple heap dumps during application execution, performs statistical analysis to identify contributing references, and calculates correlation coefficients to determine the root causes of memory leaks, allowing for real-time analysis and accurate identification of growth roots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple heap dumps are collected dynamically during application execution, then measurement precision of memory leak detection is improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent segments the complex task of memory leak detection by collecting multiple heap dumps at different time points and analyzing them separately. Each heap dump represents a snapshot of memory state, and by comparing these segmented snapshots, the system identifies objects that grow consistently across time points, thereby improving detection accuracy while managing complexity through divide-and-conquer analysis
Solution Approach 2:
The patent implements feedback mechanisms by using correlation coefficients to measure the relationship between object lifecycles and heap growth patterns. The statistical analysis provides feedback on which objects are truly leaking versus transiently growing, allowing the system to iteratively refine its identification of memory leak sources based on the measured relationships between different heap dumps
2Ease of operation
If manual analysis of heap dumps is performed, then ease of operation is maintained, but productivity and measurement precision decrease
Solution Approach 1:
The patent enables self-service analysis by implementing automated statistical methods that compute correlation coefficients between object lifecycles and heap growth patterns. The system automatically identifies growth roots and memory leak sources without requiring manual inspection of each heap dump, thereby dramatically improving productivity while maintaining ease of operation through automated report generation
Solution Approach 2:
The patent replaces the mechanical manual analysis process with automated statistical computing. Instead of manually examining heap dumps and identifying growing objects, the system uses computational algorithms to calculate correlation coefficients and automatically determine which objects contribute to memory leaks, substituting human effort with automated mechanical computation
3Device complexity
If only one or two heap dumps are analyzed, then device complexity is reduced, but measurement precision and reliability of memory leak detection worsen
Solution Approach 1:
The patent applies periodic action by collecting heap dumps at multiple time points during application execution. Rather than relying on a single snapshot, the system periodically captures memory state at different moments, allowing it to distinguish between transient memory growth and persistent leaks through the temporal pattern of object lifecycles across these periodic measurements
Solution Approach 2:
The patent performs preliminary action by collecting multiple heap dumps before conducting the final analysis. By gathering time-series data on object creation and destruction patterns beforehand, the system prepares the necessary information to reliably distinguish between transient and persistent memory growth, ensuring accurate memory leak detection before the actual statistical analysis is performed
Data Source
AI summary
Embodiments of the invention provide systems and methods for analyzing memory heap information for investigation into a memory leak caused by an application. According to one embodiment, a method of analyzing heap data can comprise obtaining the heap data from a memory. The heap data can represent a plurality of objects of one or more classes, each object identifying a referrer instance, a field in the referrer, and a referent instance. A statistical analysis can be performed on the heap data to identify objects within the heap that are contributing to a growth of the heap. The heap can be traversed based on the referrer instance of one or more objects identified as contributing to the growth of the heap to a root object identified as not contributing to the growth of the heap.